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Journal ArticleDOI

A comparative study of Artificial Bee Colony algorithm

Dervis Karaboga, +1 more
- 01 Aug 2009 - 
- Vol. 214, Iss: 1, pp 108-132
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TLDR
Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.
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This article is published in Applied Mathematics and Computation.The article was published on 2009-08-01. It has received 2835 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Meta-optimization.

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Citations
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Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem

TL;DR: A mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the welding shop scheduling problem (WSSP) and results show that proposed model and algorithm reduces 55.17% production time comparing with the traditional algorithm and the scheduled machine allocation provides more reasonable arrangements for workers and machine loads.
Journal ArticleDOI

Hybrid differential evolution with artificial bee colony and its application for design of a reconfigurable antenna array with discrete phase shifters

TL;DR: Compared with the continuous realisation and subsequent quantisation, experimental results indicate that the performance of the discrete realisation of the phase-excitation value can be improved.
Journal ArticleDOI

A parametric simulation of the wireless power transfer with inductive coupling for electric vehicles, and modelling with artificial bee colony algorithm

TL;DR: The results show that the analyzes of the WPT circuits related to the charging efficiency can be easily calculated with the proposed expression without considering simulation, measurement and sophisticated mathematics.
Journal ArticleDOI

An Exhaustive Survey on Nature Inspired Optimization Algorithms

TL;DR: The algorithms which are discussed imitate the processes running in nature, and these process are named as “Nature Inspired Algorithms”.
Journal ArticleDOI

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

TL;DR: A new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase.
References
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Proceedings ArticleDOI

Particle swarm optimization

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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Adaptation in natural and artificial systems

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Book

Self-Organizing Maps

TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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Genetic Algorithms + Data Structures = Evolution Programs

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.